User_ID App Daily_Minutes_Spent Posts_Per_Day Likes_Per_Day
1 U_1 Pinterest 288 16 94
2 U_2 Facebook 192 14 117
3 U_3 Instagram 351 13 120
4 U_4 TikTok 21 20 117
5 U_5 LinkedIn 241 16 9
6 U_6 Twitter 464 3 137
Follows_Per_Day Engagement
1 0 110
2 15 146
3 48 181
4 8 145
5 21 46
6 30 170
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This dashboard provides an analysis of social media usage metrics, exploring how users engage with different platforms. The dataset includes daily time spent, posts, likes, and follows across various platforms, allowing for an in-depth analysis of engagement patterns and usage trends.
Some research questions include:
We will investigate the following research questions:
Column {.tabset .tabset-fade}
The analysis shows patterns in social media usage across platforms. For example, platforms with higher average daily minutes indicate higher user engagement. Further work could include:
---
title: "Social Media Usage Analysis"
author: "Scot Swanson"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
theme:
bootswatch: zephyr
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(DT)
library(ggplot2)
```
# Data Loading and Cleaning
```{r}
# Load the social media dataset
data <- read.csv("social_media_usage.csv")
# Data Cleaning: Remove any missing values
data <- na.omit(data)
# Feature Engineering: Calculate Engagement as a combination of Posts, Likes, and Follows
data <- data %>%
mutate(Engagement = Posts_Per_Day + Likes_Per_Day + Follows_Per_Day)
# Display the first few rows of the cleaned data
head(data)
```
# Introduction
Column {.tabset .tabset-fade}
### Motivation and Background
This dashboard provides an analysis of social media usage metrics, exploring how users engage with different platforms. The dataset includes daily time spent, posts, likes, and follows across various platforms, allowing for an in-depth analysis of engagement patterns and usage trends.
Some research questions include:
- Which platform has the highest average daily usage?
- What is the relationship between likes and follows?
- How do different metrics correlate with each other?
### Research Questions
We will investigate the following research questions:
- **Average daily usage per platform**: Identify which platforms have higher average engagement.
- **Likes vs. Follows Relationship**: Determine the correlation between likes and follows.
- **Correlation of metrics**: Analyze correlations between engagement metrics across platforms.
# Summary Statistics
Column {.tabset .tabset-fade}
### Distribution of Daily Minutes Spent
```{r}
ggplot(data, aes(x=Daily_Minutes_Spent)) +
geom_histogram(bins=20, fill="#1f77b4") +
labs(title="Distribution of Daily Minutes Spent",
x="Daily Minutes Spent", y="Frequency") +
theme_minimal()
```
### Average Daily Usage by Platform
```{r}
avg_usage <- data %>%
group_by(App) %>%
summarize(avg_daily_minutes = mean(Daily_Minutes_Spent)) %>%
arrange(desc(avg_daily_minutes))
ggplot(avg_usage, aes(x=reorder(App, -avg_daily_minutes), y=avg_daily_minutes)) +
geom_bar(stat="identity", fill="#2ca02c") +
labs(title="Average Daily Minutes Spent by Platform",
x="Platform", y="Average Daily Minutes") +
theme_minimal()
```
# Correlation
### Correlation Matrix
```{r}
correlation_matrix <- cor(data %>% select(Daily_Minutes_Spent, Posts_Per_Day, Likes_Per_Day, Follows_Per_Day, Engagement))
plot_ly(
z = ~correlation_matrix,
x = colnames(correlation_matrix),
y = rownames(correlation_matrix),
type = "heatmap",
colorscale = "Viridis"
) %>%
layout(title = "Correlation Matrix for Social Media Usage Metrics")
```
# Exploration
### Engagement Across Platforms
```{r}
fig <- plot_ly(data, x = ~App, y = ~Engagement, type = "scatter", mode = "markers",
marker = list(size = ~Daily_Minutes_Spent, color = ~App)) %>%
layout(title = "Engagement across Social Media Platforms",
xaxis = list(title = "Platform"),
yaxis = list(title = "Engagement"))
fig
```
### Likes vs Follows
```{r}
ggplot(data, aes(x=Likes_Per_Day, y=Follows_Per_Day, color=App)) +
geom_point(size=3, alpha=0.6) +
labs(title="Relationship Between Likes and Follows",
x="Likes Per Day", y="Follows Per Day") +
theme_minimal()
```
# Conclusion
### Summary and Further Work
The analysis shows patterns in social media usage across platforms. For example, platforms with higher average daily minutes indicate higher user engagement. Further work could include:
- **Detailed time series analysis**: Studying how engagement changes over time.
- **User segmentation analysis**: Analyzing engagement by user demographics.
- **External factors**: Incorporating other datasets to enhance insights.
# References
- [Statista: Social Media Usage](https://www.statista.com/topics/1164/social-networks/)
- [Buffer Library: Social Media Engagement](https://buffer.com/library/social-media-engagement/)